Asynchronous events occur during mechanical ventilation when a patient's intrinsic respiratory rhythm fails to entrain to machine inflation or when ventilatory support is inadequate to meet the patient's requirements. Patient-ventilator asynchrony is a condition that affects a significant proportion of patients undergoing mechanical ventilation. It may be present either at the beginning of inspiration (trigger asynchrony), when the inspiratory efforts of the patient and the ventilator are out of phase. It also may be present during expiration should the inspiratory flow provided by the ventilator stop before or after the patient's own inspiratory effort. Patient-ventilator asynchrony is a common occurrence in mechanically ventilated patients, in particular those with acute or severe lung injury. Poorly synchronized patients may develop respiratory muscle fatigue, remain on mechanical ventilation longer and appear to have worse outcomes. As used herein patient includes any subject, and is not limited to humans.
Appropriate ventilatory support depends to a great extent on the reliable recognition of ventilator asynchrony; however, this is not a simple task. Perhaps the most reliable method presently available to detect asynchrony is the placement of a balloon catheter in the esophagus to measure intra-thoracic pressure changes during the breath cycle. Electromyography also has been used to assess asynchrony by comparing ventilatory muscle electrical activity to the initiation of ventilator-delivered inspiratory flow. Both methods have the obvious disadvantage of being invasive and not well tolerated by many patients, in particular those who are alert and awake. Further, they are relatively complex and require considerable operator experience. Non-invasive methods to establish the degree of patient-ventilator synchronicity have been proposed as possible alternatives to electromyography and measures of intrathoracic pressures. Perhaps the method with the widest clinical acceptance is the asynchrony index (AI) initially described by Varon J, Fromm R, Rodarte J, Reinoso M. “Prevalence of patient-ventilator asynchrony in critically ill patients.” Chest 1994; 106:141S. Although useful as a research tool, the AI method is not easily applied to monitoring patient-ventilator asynchrony since it is laborious and operator dependent.
While assessing the incidence of patient-ventilator asynchrony during mechanical ventilation, one study found asynchrony in approximately 25% of patients. Thille A W, Rodriguez P, Cabello B, Lellouche F, Brochard L., “Patient-ventilator asynchrony during assisted mechanical ventilation.” Intensive Care Med. 2006; 32:1515-1522. This condition was associated with significantly longer duration of mechanical ventilation. In a subsequent study they eliminated ineffective triggering in two-thirds of asynchronous cases by decreasing pressure support or inspiratory duration. More recently, the use of a neurally adjusted ventilator assist to control the timing and pressure of assisted delivery has been shown to decrease asynchrony by reducing triggering and cycling delays. Examples of two such studies are described in Spahija J, de Marchie M, Albert M, Bellemare P, Delisle S, Beck J, Sinderby C., “Patient-ventilator interaction during pressure support ventilation and neutrally adjusted ventilatory assist.” Crit. Care Med. 2010; 38:518-526; and Terzi N, Pelieu I, Guittet L, Ramakers M, Seguin A, Daubin C, Charbonneau P, du Cheyron D, Lofaso F. “Neurally adjusted ventilatory assist in patients recovering spontaneous breathing after acute respiratory distress syndrome: Physiological evaluation.” Crit. Care Med. 2010; 38:1830-1837.
Several noninvasive methods have been developed to automate the evaluation of asynchrony detection. These methods analyze airway signals searching for anomalies indicative of ineffective patient triggering (IT). Mulqueeny Q, Ceriana P, Carlucci A, Fanfulla F, Delmastro M, Nava S. “Automatic detection of ineffective triggering and double triggering during mechanical ventilation.” Int Care Med 2007; 33:2014-2018 proposed applying a noise filter and an unintentional leak compensation algorithm to the flow and pressure curves, followed by the calculation of the first and second derivatives of the flow signal. They tested their method in twenty mechanically ventilated patients and found 91% sensitivity and 97% specificity when compared to the manually derived AI.
Cuvelier A, Achour L, Rabarimanantsoa H, Letellier C, Muir J-F, Fauroux B. “A noninvasive method to identify ineffective triggering in patients with noninvasive pressure support ventilation.” Respiration 2010; 80:198-206, developed a complex algorithm that analyzed phase portraits, a geometrical depiction of temporal changes in patient-ventilator interaction. They were able to identify 95% of all IT efforts when comparing the results of this method to esophageal tracings in fourteen children with cystic fibrosis on non-invasive ventilation.
Chen C W, Lin W C, Hsu C H, Cheng K S, Lo C S. “Detecting ineffective triggering in the expiratory phase in mechanically ventilated patients based on airway flow and pressure deflection: feasibility of using a computer algorithm.” Crit. Care Med. 2008; 36:455-461, developed a computerized algorithm based on small deflections of the flow and pressure signals during the expiratory phase of ventilation. The algorithm detected IT with high sensitivity and specificity in fourteen ventilated patients. This method has the disadvantage of detecting only one type of patient-ventilator interaction.
Younes M, Brochard L, Grasso S, Kun J, Mancebo J, Ranieri M, Richard J C, Younes H. “A method for monitoring and improving patient: ventilator interaction.” Intensive Care Med. 2007; 33:1337-1346, monitored patient-ventilator interaction with a proprietary system that generates a signal mimicking respiratory muscle pressure output. The signal was derived from the equation of motion of the respiratory system using improvised values for resistance and elastance. This method could detect 80% of IT efforts when applied to airway signal tracings from 21 mechanically ventilated patients.
Aliasing of the airway signal with background noise may interfere with the ability of some of these methods to distinguish small deflections indicative of wasted inspiratory effort. Moreover, they may fail to identify conditions in which ventilatory support during inspiration is not sufficient to meet ventilatory requirements. Although apparently synchronous, this situation results in increased work of breathing from patient generated negative pressure efforts.